针对通用汽油机的充气技术,提出缸侧辅助充气(SAI,Side Air Injection)方法,通过优化缸内气流在中小负荷实现分层稀薄燃烧.以168F通用汽油机为原型,使用AVL-BOOST计算出初始条件,再运用AVL-FIRE仿真分析在不同辅助充气压力下SAI发动机...针对通用汽油机的充气技术,提出缸侧辅助充气(SAI,Side Air Injection)方法,通过优化缸内气流在中小负荷实现分层稀薄燃烧.以168F通用汽油机为原型,使用AVL-BOOST计算出初始条件,再运用AVL-FIRE仿真分析在不同辅助充气压力下SAI发动机对缸内速度场、湍动能和当量比的影响.分析表明:SAI发动机可在火花塞周围形成尺度大涡流强的分层混合气,可提高燃烧速率,改善汽油机稀薄燃烧着火稳定性.展开更多
The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the ...The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the spring and autumn period of 2018. The results show that 55% and 57% of the samples in spring and autumn respectively exceed the threshold fixed by WHO(50 mg/L). However, nitrate concentrations do not show seasonal and spatial variation(p>0.05). The results of the correlation matrix, principal component analysis(PCA), and hierarchical cluster analysis(HCA) suggest that nitrate pollution is related to anthropogenic source. Moreover, multiple linear regression results show that NO3 is more positively explained in the spring period by Ca and SO4 and negatively explained by pH and HCO3. Regarding the autumn period, nitrate pollution is positively explained by Ca and negatively by pH. This study proposes a useful statistical platform for assessing nitrate pollution in groundwater.展开更多
文摘针对通用汽油机的充气技术,提出缸侧辅助充气(SAI,Side Air Injection)方法,通过优化缸内气流在中小负荷实现分层稀薄燃烧.以168F通用汽油机为原型,使用AVL-BOOST计算出初始条件,再运用AVL-FIRE仿真分析在不同辅助充气压力下SAI发动机对缸内速度场、湍动能和当量比的影响.分析表明:SAI发动机可在火花塞周围形成尺度大涡流强的分层混合气,可提高燃烧速率,改善汽油机稀薄燃烧着火稳定性.
文摘The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the spring and autumn period of 2018. The results show that 55% and 57% of the samples in spring and autumn respectively exceed the threshold fixed by WHO(50 mg/L). However, nitrate concentrations do not show seasonal and spatial variation(p>0.05). The results of the correlation matrix, principal component analysis(PCA), and hierarchical cluster analysis(HCA) suggest that nitrate pollution is related to anthropogenic source. Moreover, multiple linear regression results show that NO3 is more positively explained in the spring period by Ca and SO4 and negatively explained by pH and HCO3. Regarding the autumn period, nitrate pollution is positively explained by Ca and negatively by pH. This study proposes a useful statistical platform for assessing nitrate pollution in groundwater.
